System and method for determining triage categories

ABSTRACT

Embodiments disclosed herein provide a system, method, and computer program product for providing a triage classification system. The triage classification system uses a computer model that is developed using historical patient data. The developed computer model is applied to collected patient attribute data from a patient in a pre-hospital setting to generate a triage category. Based on the generated triage category, health care professionals can take desired actions, such as transporting the patient to a facility matching the generated triage category.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of and claims a benefit of priorityunder 35 U.S.C. § 120 from U.S. application Ser. No. 16/356,864, filedMar. 18, 2019, entitled “SYSTEM AND METHOD FOR DETERMINING TRIAGECATEGORIES,” which is a continuation of and claims a benefit of priorityunder 35 U.S.C. § 120 from U.S. application Ser. No. 14/773,101, filedSep. 4, 2015, entitled “SYSTEM AND METHOD FOR DETERMINING TRIAGECATEGORIES,” now U.S. Pat. No. 10,262,108, which is a national stageapplication of International Application No. PCT/US2014/019878, filedMar. 3, 2014 entitled “SYSTEM AND METHOD FOR DETERMINING TRIAGECATEGORIES,” which claims a benefit of priority from U.S. ProvisionalApplication No. 61/772,172, filed Mar. 4, 2013, entitled “SYSTEM ANDMETHOD FOR DETERMINING TRIAGE CATEGORIES.” This application relates toInternational Application No. PCT/US2014/023329, filed Mar. 11, 2014,entitled “SYSTEM AND METHOD FOR A PATIENT DASHBOARD,” which claims abenefit of priority from U.S. Provisional Application No. 61/780,174,filed Mar. 13, 2013, entitled “SYSTEM AND METHOD FOR A PATIENTDASHBOARD.” All applications listed in this paragraph are incorporatedby reference as if set forth herein in their entireties.

TECHNICAL FIELD

This disclosure relates generally to the field of medical informatics.More specifically, the disclosure relates to the computerizeddetermination of triage categories for patients. Even more particularly,the disclosure relates to computerized assistance in the assignment of atriage category to a patient in a pre-hospital setting.

BACKGROUND OF THE RELATED ART

Typically, a particular community or geographical area has a limitednumber of trauma centers. These trauma centers are usually segmentedinto levels, with the highest level trauma centers, that are capable ofproviding comprehensive service to treat traumatic injuries, beingassigned a Level 1 designation while more limited care facilities beingassigned a Level 2 or 3 designation. Thus, trauma victims may be triaged(e.g., assigned a trauma category, which may correspond with a leveldesignation for a trauma center) based on a set of criteria. The patientcan then be transported or otherwise delivered into the care of a traumacenter with a level corresponding to the assigned category.

Trauma Center designation is a process outlined and developed at a stateor local level. The state or local municipality identifies uniquecriteria in which to categorize Trauma Centers. These categories mayvary from state to state and are typically outlined through legislativeor regulatory authority. Following are exemplary definitions of varioustrauma center levels, as defined by the American Trauma Society (ATS).Other definitions are also possible.

A Level I Trauma Center is a comprehensive regional resource that is atertiary care facility central to the trauma system. A Level I TraumaCenter is capable of providing total care for every aspect ofinjury—from prevention through rehabilitation. Elements of Level ITrauma Centers Include:

-   -   24-hour in-house coverage by general surgeons, and prompt        availability of care in specialties such as orthopedic surgery,        neurosurgery, anesthesiology, emergency medicine, radiology,        internal medicine, plastic surgery, oral and maxillofacial,        pediatric and critical care.    -   Referral resource for communities in nearby regions.    -   Provides leadership in prevention, public education to        surrounding communities.    -   Provides continuing education of the trauma team members.    -   Incorporates a comprehensive quality assessment program.    -   Operates an organized teaching and research effort to help        direct new innovations in trauma care.    -   Program for substance abuse screening and patient intervention.    -   Meets minimum requirement for annual volume of severely injured        patients.

A Level II Trauma Center is able to initiate definitive care for allinjured patients. Elements of Level II Trauma Centers Include:

-   -   24-hour immediate coverage by general surgeons, as well as        coverage by the specialties of orthopedic surgery, neurosurgery,        anesthesiology, emergency medicine, radiology and critical care.    -   Tertiary care needs such as cardiac surgery, hemodialysis and        microvascular surgery may be referred to a Level I Trauma        Center.    -   Provides trauma prevention and to continuing education programs        for staff.    -   Incorporates a comprehensive quality assessment program.

A Level III Trauma Center has demonstrated an ability to provide promptassessment, resuscitation, surgery, intensive care and stabilization ofinjured patients and emergency operations. Elements of Level III TraumaCenters Include:

-   -   24-hour immediate coverage by emergency medicine physicians and        the prompt availability of general surgeons and        anesthesiologists.    -   Incorporates a comprehensive quality assessment program    -   Has developed transfer agreements for patients requiring more        comprehensive care at a Level I or Level II Trauma Center.    -   Provides back-up care for rural and community hospitals.    -   Offers continued education of the nursing and allied health        personnel or the trauma team.    -   Involved with prevention efforts and must have an active        outreach program for its referring communities.

A Level IV Trauma Center has demonstrated an ability to provide advancedtrauma life support (ATLS) prior to transfer of patients to a higherlevel trauma center. It provides evaluation, stabilization, anddiagnostic capabilities for injured patients. Elements of Level IVTrauma Centers Include:

-   -   Basic emergency department facilities to implement ATLS        protocols and 24-hour laboratory coverage.    -   Available trauma nurse(s) and physicians available upon patient        arrival.    -   May provide surgery and critical-care services if available.    -   Has developed transfer agreements for patients requiring more        comprehensive care at a Level I or Level II Trauma Center.    -   Incorporates a comprehensive quality assessment program    -   Involved with prevention efforts and must have an active        outreach program for its referring communities.

A Level V Trauma Center provides initial evaluation, stabilization anddiagnostic capabilities and prepares patients for transfer to higherlevels of care. Elements of Level IV Trauma Centers Include:

-   -   Basic emergency department facilities to implement ATLS        protocols    -   Available trauma nurse(s) and physicians available upon patient        arrival.    -   After-hours activation protocols if facility is not open        24-hours a day.    -   May provide surgery and critical-care services if available.    -   Has developed transfer agreements for patients requiring more        comprehensive care at a Level I though III Trauma Centers.

A facility can be designated an adult trauma center, a pediatric traumacenter, or an adult and pediatric trauma center. If a hospital providestrauma care to both adult and pediatric patients, the Level designationmay not be the same for each group. For example, a Level 1 adult traumacenter may also be a Level 2 pediatric trauma center. This is becausepediatric trauma surgery is a specialty unto itself.

Accurately triaging patients is difficult. In most cases, the triagingoccurs in a pre-hospital setting such as when first responders oremergency medical service personnel are assessing or transporting thepatient. Appropriate triage of trauma patients is vital for efficientutilization of trauma resources and the delivery of appropriate care.Under triage (assignment of a lower triage category to a patient thatshould be assigned a higher triage category) is extremely problematic,as the patient may not receive appropriate care. Over triage (assignmentof a higher triage category to a patient that should be assigned a lowertriage category) can also be problematic by forcing patients out oftheir community unnecessarily, wasting resources, and delaying treatmentfor those critically injured.

SUMMARY OF THE DISCLOSURE

Embodiments disclosed herein provide a system and method for determiningtriage categories for patients. In some embodiments, a computer model isdeveloped using a training data set comprised of historical patientdata. The computer model is applied to collected patient attribute datafrom a patient in a pre-hospital setting to generate a triage category.

Embodiments disclosed herein can provide many advantages. For example,over triage rates can be reduced while not significantly increasingunder triage rates. The computer model can be tuned to achieve desiredover triage and under triage rates. The computer model can also betrained using patient data from specific geographic regions.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings accompanying and forming part of this specification areincluded to depict certain aspects of the invention. A clearerimpression of the invention, and of the components and operation ofsystems provided with the invention, will become more readily apparentby referring to the exemplary, and therefore nonlimiting, embodimentsillustrated in the drawings, wherein identical reference numeralsdesignate the same components. Note that the features illustrated in thedrawings are not necessarily drawn to scale.

FIG. 1 is a block diagram of one embodiment of a topology of a triagesystem.

FIG. 2 is a flow diagram representing one embodiment of a method fordetermining a triage category.

FIG. 3 is a flow chart depicting a process for developing a triageclassification model.

FIG. 4 is a flow chart depicting a process of applying a developedtriage classification model to patient data.

DETAILED DESCRIPTION

The invention and the various features and advantageous details thereofare explained more fully with reference to the nonlimiting embodimentsthat are illustrated in the accompanying drawings and detailed in thefollowing description. Descriptions of well-known starting materials,processing techniques, components and equipment are omitted so as not tounnecessarily obscure the invention in detail. It should be understood,however, that the detailed description and the specific examples, whileindicating preferred embodiments of the invention, are given by way ofillustration only and not by way of limitation. Various substitutions,modifications, additions and/or rearrangements within the spirit and/orscope of the underlying inventive concept will become apparent to thoseskilled in the art from this disclosure. Embodiments discussed hereincan be implemented in suitable computer-executable instructions that mayreside on a computer readable medium (e.g., a HD), hardware circuitry orthe like, or any combination.

Before discussing specific embodiments, embodiments of a hardwarearchitecture for implementing certain embodiments is generally describedherein and will be discussed in more detail later. One embodiment caninclude one or more computers communicatively coupled to a network. Asis known to those skilled in the art, the computer can include a centralprocessing unit (“CPU”), at least one read-only memory (“ROM”), at leastone random access memory (“RAM”), at least one hard drive (“HD”), andone or more input/output (“I/O”) device(s). The I/O devices can includea keyboard, monitor, printer, electronic pointing device (such as amouse, trackball, stylus, etc.), or the like. In various embodiments,the computer has access to at least one database over the network.

ROM, RAM, and HD are tangible computer readable medium for storingcomputer-executable instructions executable by the CPU. Within thisdisclosure, the term “computer-readable medium” is not limited to ROM,RAM, and HD and can include any type of data storage medium that can beread by a processor. In some embodiments, a tangible computer-readablemedium may refer to a data cartridge, a data backup magnetic tape, afloppy diskette, a flash memory drive, an optical data storage drive, aCD-ROM, ROM, RAM, HD, or the like.

At least portions of the functionalities or processes described hereincan be implemented in suitable computer-executable instructions. Thecomputer-executable instructions may be stored as software codecomponents or modules on one or more computer readable media (such asnon-volatile memories, volatile memories, DASD arrays, magnetic tapes,floppy diskettes, hard drives, optical storage devices, etc. or anyother appropriate computer-readable medium or storage device). In oneembodiment, the computer-executable instructions may include lines ofcomplied C++, Java, HTML, or any other programming or scripting code.

Additionally, the functions of the disclosed embodiments may beimplemented on one computer or shared/distributed among two or morecomputers in or across a network. Communications between computersimplementing embodiments can be accomplished using any electronic,optical, radio frequency signals, or other suitable methods and tools ofcommunication in compliance with known network protocols.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,process, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, process,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

Additionally, any examples or illustrations given herein are not to beregarded in any way as restrictions on, limits to, or expressdefinitions of, any term or terms with which they are utilized. Instead,these examples or illustrations are to be regarded as being describedwith respect to one particular embodiment and as illustrative only.Those of ordinary skill in the art will appreciate that any term orterms with which these examples or illustrations are utilized willencompass other embodiments which may or may not be given therewith orelsewhere in the specification and all such embodiments are intended tobe included within the scope of that term or terms. Language designatingsuch nonlimiting examples and illustrations includes, but is not limitedto: “for example,” “for instance,” “e.g.,” “in one embodiment.”

A brief discussion of context, particularly with respect to trauma, maybe helpful. According to recent Centers for Disease Control andPrevention (CDC) statistics, trauma accounts for more than 42 millionemergency department visits and more than 2 million hospital admissionsevery year in the United States. As mentioned above, in most cases, aparticular community or geographical area has a limited number of traumacenters (e.g., locations where trauma patients may be treated, such ashospitals or the like). These trauma centers are usually segmented intolevels, with the highest level trauma centers, that are capable ofproviding comprehensive service to treat traumatic injuries, beingassigned a Level 1 designation while more limited care facilities beingassigned a Level 2 or 3 designation (or lower, as in some cases statesmay have their own ranking scale for such trauma centers) based on theircapabilities. As patients come into a system, they may be triaged basedon a set of criteria. The patients can then be transported or otherwisedelivered into the care of a trauma center with a triage levelcorresponding to the assigned category.

The appropriate triage of trauma patients is vital for efficientutilization of trauma resources and the delivery of appropriate care.Under triage (assignment of a lower triage category to a patient thatshould be assigned a higher triage category and delivering the patientto a trauma center associated with the lower category) is extremelyproblematic, as the patient may not receive appropriate care. While suchunder triage can have devastating consequences, over triage (assigning apatient a higher triage category than the patient requires anddelivering the patient to a higher level trauma center) can also beproblematic by forcing patients out of their community unnecessarily,wasting resources and delaying, treatment for those critically injured.These problems may be especially critical in a mass emergency/casualtysettings such as during severe weather accidents involving mass transit,terrorist attacks, etc. where it may especially important to distributepatients across trauma centers such that resources can be appliedappropriately.

As mentioned above, accurately triaging patients is difficult. In mostcases, the triaging (e.g., assignment of a trauma category) occurs in apre-hospital setting, such as when first responders or emergency medicalservice personnel are assessing or transporting the patient. In manyinstances, to make such an assignment, a checklist may be used, wherethe checklist specifies values for one or more criteria (e.g., valuesfor vital signs, location or type of trauma, etc.). The patient isassigned to a trauma category based on this checklist and on values forthe one or more criteria associated with that patient.

There are a number of problems with current techniques of assigningtrauma categories to patients. One problem stems from the variability ofchecklists across geographic regions and care providers. In some cases,this variability results in care providers, such as first responders ortransport providers, using different checklists within the same locale(e.g., when the options for treatment for each of those patients is thesame).

Furthermore, such checklists may be restricted to the use of only fewcriteria (e.g., for various reasons, such as complexity of determinationof values for these criteria, time concerns in the applicability of suchchecklists, etc.) and thus, the granularity of assignment of triagecategories may be rather large. For example, in most instances, thereare only two types of these checklists, one to apply to adults and oneto used assign triage levels to children. Additionally, traumacategories are assigned mainly based on three domains: physiology,mechanism of injury, and anatomical location of injury. These domainsmay be defined during an initial physical exam of the patient in apre-hospital environment. In some instances, these three domains may beinsufficient for accurate category assignment.

Moreover, as it may be desired to err on the side of caution, thesechecklists may be inherently constructed to bias the assignment oftriage category to a patient to substantially eliminate instances ofunder triage. Of course, such a bias may result in over triage. In fact,in 1999 a generally accepted over triage rate of around 50% wasestablished. While this over triage rate has been re-evaluated it hasreally never been broadly and effectively reduced. Consequently, it isdesired to substantially reduce this over triage rate, while notsubstantially increasing (or increasing at all) the under triage rate.

Accordingly, attention is directed to the systems and methods fordetermining triage categories for patients depicted herein.Specifically, embodiments of such triage systems and methods may beemployed in certain settings (e.g., pre-hospital) to more accuratelyassign a triage category to a patient based on multiple variablesassociated with a patient. Such systems may employ a model to provide atriage category recommendation based on data for the multiple variablesobtained for the patient. In some examples, the model is created, testedand tuned based on patient data obtained from previously triagedpatients. Thus, by using the model, a more accurate triage category maybe assigned to a patient. Furthermore, a triage system may be developedbased on data collected in a particular environment, resulting in atriage system tailored to that particular environment (e.g., thedifferent triage levels present in a geographic region, etc.). In someexamples, it has been determined that a triage system as depicted hereinmay reduce the over triage rate by approximately 24%, whilesubstantially maintaining the under triage rate.

Embodiments of such systems and methods may be better understood withreference to FIG. 1 , which depicts one embodiment of a topology inwhich a triage system may be developed and deployed. FIG. 1 depictssystem 1000, including a trauma center 1010 (for example, a hospital)that accept patients 1020. The trauma center 1010 may have its ownassigned triage level (e.g., level 1, level 2, etc.) and may acceptpatients that have been assigned a triage category in a pre-hospitalsetting 1030. Examples of pre-hospital settings in which a triagecategory is assigned include during transport to the trauma center 1010,at some other point by first responders, during patient intake at thetrauma center 1010, etc. It should be understood that, while embodimentsas described herein may be described in conjunction with their use in apre-hospital environment, other embodiments may also be applied in othersettings in which it is desired to assign a triage category to apatient.

When a patient is received at the trauma center 1010, patient data 1040is collected and maintained. The patient data 1040 may be collected atthe trauma center by elements within the trauma center (e.g., a researchbranch) or some other entity (e.g., a 3^(rd) party consultant orresearch group, etc.). The patient data 1040 may include the triagecategory the patient was assigned in the pre-hospital setting, vitalsigns determined at multiple points in the pre-hospital environment,patient demographics, mechanism of injury, interventions (e.g.,pre-hospital fluid, medications), patient management characteristics,site of injury, disposition (e.g., the accuracy of the initial triagelevel assigned in the pre-hospital setting, a triage level to which thepatient should have been assigned, for example, based on patient outcomeor treatment, etc.), bleeding status, pulse character, or otherattributes. As one skilled in the art would understand, there may benumerous attributes on which data is collected (e.g., on the order of 80or more).

As shown in block 1050 of FIG. 1 , the patient data 1040 collected fromnumerous patients may be used to develop a model for assigning a triagelevel to a patient. A model 1060 may be, for example, a statisticalmodel that includes a classifier 1070. The classifier 1070 may beconfigured to take as input one or more values for attributes collectedfrom a patient. Based on the values of those attributes, the model 1060determines a triage category for the patient. In some embodiments, theclassifier may be able to effectively treat the attributes asfunctionally continuous. The classifier can therefore catch subtledifferences in values of the collected attributes that may result indifference in triage categorization even though actual differences ofthe values in the data set may not be large.

The classifier is able to deal with a large number of input variablesand work with incomplete data (e.g., determine a triage level based onfewer attributes than were used to develop the classifier). In addition,some embodiments of classifiers may use multiple techniques to achieve amore accurate assignment of triage level relative to the use of simpler,or a single, technique.

Accordingly, in one embodiment, a classifier may be comprised of anensemble classifier such as random forest, rotation forest, LogitBoost(e.g., additive logistic regression, random subspace alternatingdecision trees and learns alternating decision trees using LogitBoost).These types on ensemble classifiers are listed by way of example, and itshould be understood that others may also be used. In some examples, theuse of an ensemble classifier may work better than other classifiersthat may be utilized (e.g., logistic regression, naive Bayesiananalysis, multilayer perceptron), as these simpler techniques givepoorer results when faced with a complex system. Ensemble classifiersuse multiple techniques to achieve a more accurate classification, whilesimpler techniques such as Bayes and logistic regression may be limitedto one type of analysis. Using multiple techniques allows for anincreased size of data input parameters and often produces astatistically significant predictor when other, simpler techniques maynot work as well.

In one embodiment, the classifier may be comprised of a random forestclassifier algorithm for pre-hospital triage of patients. Such aclassifier takes inputs (e.g., values for attributes, including, forexample, vital signs) and determine an appropriate triage category. Sucha random forest classifier may be an ensemble classifier that uses acombination of many decision trees with the number of votes from all (ora subset of votes from the decision trees) determining the appropriatelevel of triage. Each tree depends on the values of a random vectorsampled independently and with the same distribution for all trees inthe forest.

In some embodiments, after a model is developed, the classifier may betuned according to a desired over triage rate or a desired under triagerate. The tuning of a classifier may be accomplished using costsensitization, pruning or out of bag error such that the assignment of atriage level based on the classifier may be more likely to yield thedesired under triage or over triage rate.

Referring again to FIG. 1 , once the model 1060 has been developed, itmay be deployed or otherwise utilized in a triage system 1080. A triagesystem 1080 may be employed in a pre-hospital setting. The triage system1080 includes a triage module 1090 having an interface 1100 and themodel 1060. The interface 1100 may accept inputs such as values for avariety of attributes. These inputs may be input manually (e.g., by afirst responder or other operator of the triage system 1080) or may beobtained through the interface automatically (e.g., from variousmonitors or other devices, for example, using the HL7 protocol or thelike).

Using the inputs received through the interface 1100, the triage module1090 may use the model 1060 to determine a triage category for apatient. The determined triage category may then be presented throughthe interface 1100 to the operator of the triage system 1080. The triagesystem 1080 may reside in any desired type of computing device, such asa smartphone, a laptop, etc. Once a triage category has been determinedfor a patient, the appropriate action for that classification isundertaken. For example, the patient may be transported (via anambulance 1110 or other vehicle) to a facility chosen based on thetriage classification.

Devices already present in a first responder setting (e.g., onambulances, medical helicopters, fire trucks, etc.) such patientmonitors, etc. are computing devices having a processor, storage,display, etc. In one embodiment, such a device may be configured toinclude the triage system 1080 or a triage module 1090 in a fairly easyor straightforward manner. Therefore, a triage classification system asdepicted herein may be integrated into a pre-hospital setting withoutadditional equipment or space requirements. As a result, these types ofembodiments provide desired pre-hospital triaging recommendationswithout a significant increase in cost or complexity.

FIG. 2 is a flow chart of one embodiment of a method for developing anddeploying a triage system. Initially, at step 2010, patient data isdetermined. In one example, the patient data is collected in apre-hospital setting. To develop a model, patient data is collected froma plurality of patients. In one embodiment, the patient data iscollected in the same pre-hospital setting for which it is desired toimplement the triage system, such that the developed triage system willbe tailored to that particular pre-hospital setting. In one example,patient data is collected from patients transported to, or treated at,trauma center(s) within a geographical region which have been assigned atriage category. The collected patient data may include the triagecategory the patient was assigned in the pre-hospital setting, vitalsigns determined at multiple points in the pre-hospital environment,patient demographics, mechanism of injury, interventions (e.g.,pre-hospital fluid, medications), patient management characteristics,site of injury, disposition (e.g., the accuracy of the initial triagelevel assigned in the pre-hospital setting, a triage level to which thepatient should have been assigned, for example, based on patient outcomeor treatment, etc.), or other attributes. As one skilled in the artwould understand, data can be collected relating to numerous attributes(e.g., on the order of 80 or more).

Once the patient data is obtained (step 2010) a model is created at step2020 based on the collected data. The model may include a classifierconfigured to input patient data and provide a recommended triagecategory. To construct such a classifier the collected data may besegmented into a training set (e.g., 70% of the data) and a testing set(e.g., 30% of the data). Using this data, a statistical model thatincludes a classifier may be created. Such a classifier may beconfigured to take as input one or more values for attributes, and basedon the value for those attributes, determine a triage category for thepatient. In some embodiments, classifiers may be able to effectivelytreat these attributes as functionally continuous. The classifier cantherefore catch subtle differences in values of the collected attributesthat may result in difference in triage categorization even thoughactual differences of the values in the data set may not be large.

Optionally, at step 2030, the model may be tuned. In the tuning process,a desired under triage or over triage level may be set (e.g., apercentage). Such levels may be set according to, or tailored to avariety of criteria, including geography where the system is to bedeployed, a care network, etc. The tuning of a classifier may beaccomplished using cost sensitization, pruning or out of bag error suchthat the assignment of a triage level based on the classifier may bemore likely to yield the desired under triage or over triage rate.

The model, including the classifier, may then be integrated into apre-hospital triage system and deployed in a pre-hospital setting atstep 2040. Specifically, such a triage system may have an interface foraccepting inputs, which may include values for patient attributes. Thetriage system may then use the model to determine a triage category fora patient. This triage category may then be presented through theinterface to the operator of the triage system.

Once the model is integrated into the pre-hospital setting it may beutilized to obtain a triage category recommendation in a pre-hospitalenvironment (step 2050). An operator, such a first responder or thelike, caring for a patient may manually provide inputs corresponding tothe patient in a pre-hospital setting (e.g., in an ambulance or medicalhelicopter) to the triage system. Inputs may also be obtained throughthe interface automatically. The inputs may comprise values for one ormore of the types of attributes that were used to create the model ofthe triage system.

Using the inputs received through the interface, the triage system usesthe model to determine a triage category for the patient. The determinedtriage category may then be presented through the interface to theoperator of the triage system. Additionally, in some embodiments, someamount of data used in determining the triage category (e.g.,percentages of decision trees corresponding to the recommended triagecategory) may be presented through the interface.

It will be noted here that utilizing the model, the triage system maymake triage category recommendations using fewer attributes that wereused to create the model. Thus, no matter the number of attributesassociated with the patient data used to create the model used in thetriage system, a triage category recommendation may be provided by thetriage system based on values for one or more of those attributes. Asadditional values for attributes, or values for additional attributesare obtained, these may be provided through the interface and a newrecommended triage category (which may be the same or different)determined and presented to the operator.

According to the environment in which the triage system is deployed, theoperator may be required to follow to the recommendation of the triagesystem and transport the patient to a trauma center corresponding to therecommended category or alternatively, the operator may utilize therecommended triage category for the patient in making his own decisionabout which level of trauma center to transport the patient to.

Using the data obtained when utilizing the triage system with the modelin the pre-hospital setting (or other patient data), the algorithm mayoptionally be refined or updated at step 2060. In this manner, thetriage category recommendations may be constantly improved or refined.

FIG. 3 is a flow chart depicting a process for developing a triageclassification model. At step 3010, historical patient data is gathered.The patient data can be gathered as new patients arrive at a facility,and/or retrieved from storage. As described above, the patient data caninclude any desired available data, including the triage category thepatient was assigned in the pre-hospital setting, vital signs, patientdemographics, mechanism of injury, interventions, patient managementcharacteristics, site of injury, and other desired attributes.

From the gathered historical patient data, a set of training data isconfigured (step 3020). At step 3030, a set of validation data isconfigured. In one example, a certain percentage of the collected datais used as training data, and a certain percentage of the collected datais used as validation data. At step 3040, the model is developed usingthe training data. Each training data item includes input variables(vital signs, etc.) and an answer (the proper triage classification). Inone example, a machine learning model is trained using the training datauntil the model is able to determine the answer (in this example, atriage classification) based on the input attributes. At step 3050, thetrained model uses the validation data to test and validate the model.For example, if the model takes the inputs of the validation data andgenerates the correct outputs, then it can be determined that the modelis adequately trained. If desired, the model can be tuned (step 3060).After the model is developed, the classifier may be tuned according to adesired over triage rate or a desired under triage rate.

FIG. 4 is a flow chart depicting a process of applying a developedtriage classification model to patient data. When a patient is broughtto a pre-hospital setting (e.g., a setting such as when first respondersor emergency medical service personnel are assessing or transporting thepatient) a patient intake process begins (step 4010). During patientintake, patient data is collected (step 4020), such as vital signs,symptoms, patient demographics, mechanism of injury, interventions, siteof injury, etc. Next, the model is applied to the collected patient data(step 4030). From the input data, the model generates a triageclassification score (step 4040), which, in one example, can be a numberwithin a range of numbers. Based on desired over and under triage rates,triage guidelines, etc., the model is configured to take the triageclassification score and generate a triage classification (step 4050).Once the patient has a triage classification, the health careprofessionals can act on the generated classification (step 4060).Actions taken in response a triage classification typically relate totransporting the patient to a health care facility that matches thetriage level indicated by the model. Other actions are also possible. Inthe example of FIG. 4 , the patient is transported to an appropriatehealth care facility (step 4070). Depending on the triageclassification, the patient is transported to a trauma center level 1facility (step 4080), a trauma center level 2 facility (step 4090), or atrauma center level 3 facility (step 4100). Note that while FIG. 4 showsan example with three levels of facilities, there may be more (or fewer)levels available. In addition, adult and pediatric designations may alsobe used.

Note that, while a model developed for one specific setting (e.g., ageographic area) can be used in other settings, although for bestresults, a new model can be developed for the new settings. For example,a model developed using data collected in a first city may not work aswell in a second city as a model developed in the second city.

In the foregoing specification, the invention has been described withreference to specific embodiments. However, one of ordinary skill in theart appreciates that various modifications and changes can be madewithout departing from the scope of the invention. Accordingly, thespecification and figures are to be regarded in an illustrative ratherthan a restrictive sense, and all such modifications are intended to beincluded within the scope of invention.

Although the invention has been described with respect to specificembodiments thereof, these embodiments are merely illustrative, and notrestrictive of the invention. The description herein of illustratedembodiments of the invention is not intended to be exhaustive or tolimit the invention to the precise forms disclosed herein (and inparticular, the inclusion of any particular embodiment, feature orfunction is not intended to limit the scope of the invention to suchembodiment, feature or function). Rather, the description is intended todescribe illustrative embodiments, features and functions in order toprovide a person of ordinary skill in the art context to understand theinvention without limiting the invention to any particularly describedembodiment, feature or function. While specific embodiments of, andexamples for, the invention are described herein for illustrativepurposes only, various equivalent modifications are possible within thespirit and scope of the invention, as those skilled in the relevant artwill recognize and appreciate. As indicated, these modifications may bemade to the invention in light of the foregoing description ofillustrated embodiments of the invention and are to be included withinthe spirit and scope of the invention. Thus, while the invention hasbeen described herein with reference to particular embodiments thereof,a latitude of modification, various changes and substitutions areintended in the foregoing disclosures, and it will be appreciated thatin some instances some features of embodiments of the invention will beemployed without a corresponding use of other features without departingfrom the scope and spirit of the invention as set forth. Therefore, manymodifications may be made to adapt a particular situation or material tothe essential scope and spirit of the invention.

Reference throughout this specification to “one embodiment,” “anembodiment,” or “a specific embodiment” or similar terminology meansthat a particular feature, structure, or characteristic described inconnection with the embodiment is included in at least one embodimentand may not necessarily be present in all embodiments. Thus, respectiveappearances of the phrases “in one embodiment,” “in an embodiment,” or“in a specific embodiment” or similar terminology in various placesthroughout this specification are not necessarily referring to the sameembodiment. Furthermore, the particular features, structures, orcharacteristics of any particular embodiment may be combined in anysuitable manner with one or more other embodiments. It is to beunderstood that other variations and modifications of the embodimentsdescribed and illustrated herein are possible in light of the teachingsherein and are to be considered as part of the spirit and scope of theinvention.

In the description herein, numerous specific details are provided, suchas examples of components and/or methods, to provide a thoroughunderstanding of embodiments of the invention. One skilled in therelevant art will recognize, however, that an embodiment may be able tobe practiced without one or more of the specific details, or with otherapparatus, systems, assemblies, methods, components, materials, parts,and/or the like. In other instances, well-known structures, components,systems, materials, or operations are not specifically shown ordescribed in detail to avoid obscuring aspects of embodiments of theinvention. While the invention may be illustrated by using a particularembodiment, this is not and does not limit the invention to anyparticular embodiment and a person of ordinary skill in the art willrecognize that additional embodiments are readily understandable and area part of this invention.

Any suitable programming language can be used to implement the routines,methods or programs of embodiments of the invention described herein,including C, C++, Java, assembly language, etc. Different programmingtechniques can be employed such as procedural or object oriented. Anyparticular routine can execute on a single computer processing device ormultiple computer processing devices, a single computer processor ormultiple computer processors. Data may be stored in a single storagemedium or distributed through multiple storage mediums, and may residein a single database or multiple databases (or other data storagetechniques). Although the steps, operations, or computations may bepresented in a specific order, this order may be changed in differentembodiments. In some embodiments, to the extent multiple steps are shownas sequential in this specification, some combination of such steps inalternative embodiments may be performed at the same time. The sequenceof operations described herein can be interrupted, suspended, orotherwise controlled by another process, such as an operating system,kernel, etc. The routines can operate in an operating system environmentor as stand-alone routines. Functions, routines, methods, steps andoperations described herein can be performed in hardware, software,firmware or any combination thereof.

Embodiments described herein can be implemented in the form of controllogic in software or hardware or a combination of both. The controllogic may be stored in an information storage medium, such as acomputer-readable medium, as a plurality of instructions adapted todirect an information processing device to perform a set of stepsdisclosed in the various embodiments. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the invention.

It is also within the spirit and scope of the invention to implement insoftware programming or of the steps, operations, methods, routines orportions thereof described herein, where such software programming orcode can be stored in a computer-readable medium and can be operated onby a processor to permit a computer to perform any of the steps,operations, methods, routines or portions thereof described herein. Theinvention may be implemented by using software programming or code inone or more general purpose digital computers, by using applicationspecific integrated circuits, programmable logic devices, fieldprogrammable gate arrays, optical, chemical, biological, quantum ornanoengineered systems, components and mechanisms may be used. Ingeneral, the functions of the invention can be achieved by any means asis known in the art. For example, distributed, or networked systems,components and circuits can be used. In another example, communicationor transfer (or otherwise moving from one place to another) of data maybe wired, wireless, or by any other means.

A “computer-readable medium” may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, system ordevice. The computer readable medium can be, by way of example, only butnot by limitation, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, system, device,propagation medium, or computer memory. Such computer-readable mediumshall generally be machine readable and include software programming orcode that can be human readable (e.g., source code) or machine readable(e.g., object code).

A “processor” includes any, hardware system, mechanism or component thatprocesses data, signals or other information. A processor can include asystem with a general-purpose central processing unit, multipleprocessing units, dedicated circuitry for achieving functionality, orother systems. Processing need not be limited to a geographic location,or have temporal limitations. For example, a processor can perform itsfunctions in “real-time,” “offline,” in a “batch mode,” etc. Portions ofprocessing can be performed at different times and at differentlocations, by different (or the same) processing systems.

It will also be appreciated that one or more of the elements depicted inthe drawings/figures can also be implemented in a more separated orintegrated manner, or even removed or rendered as inoperable in certaincases, as is useful in accordance with a particular application.Additionally, any signal arrows in the drawings/figures should beconsidered only as exemplary, and not limiting, unless otherwisespecifically noted.

Furthermore, the term “or” as used herein is generally intended to mean“and/or” unless otherwise indicated. As used herein, a term preceded by“a” or “an” (and “the” when antecedent basis is “a” or “an”) includesboth singular and plural of such term (i.e., that the reference “a” or“an” clearly indicates only the singular or only the plural). Also, asused in the description herein, the meaning of “in” includes “in” and“on” unless the context clearly dictates otherwise.

Benefits, other advantages, and solutions to problems have beendescribed above with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any component(s) thatmay cause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeature or component.

What is claimed is:
 1. A computer-implemented triaging method,comprising: receiving, by a computer, patient data from devices in apre-hospital setting, the patient data describing a patient in thepre-hospital setting, the computer having a machine learning (ML) modelfor assigning a triage category to the patient based on a plurality ofinput variables associated with the patient, wherein the ML model istrained with the plurality of input variables based on historicalpatient data that describe patients who had been assigned triagecategories in at least one pre-hospital setting prior to the patientsbeing transported or delivered into care of one or more trauma centers,each of the one or more trauma centers having a level designationcorresponding to a respective one of the triage categories; applying, bythe computer, the ML model to the patient data received from the devicesin the pre-hospital setting, wherein the applying comprises processingthe plurality of input variables associated with the patient andgenerating a triage category for the patient in the pre-hospital settingbased on the processing; and presenting, by the computer via a userinterface, the triage category generated for the patient in thepre-hospital setting for transporting or delivering the patient intocare of a trauma center having a level designation corresponding to thetriage category generated for the patient.
 2. The method according toclaim 1, wherein the ML model comprises a statistical model thatincludes a classifier configured for taking as input one or moreattributes collected from the patient and determining a triage categoryfor the patient based on the one or more attributes collected from thepatient.
 3. The method according to claim 2, wherein the classifier isconfigured for processing a number of input variables fewer than theplurality of input variables and for generating a triage category forthe patient in the pre-hospital setting based on the number of inputvariables associated with the patient.
 4. The method according to claim1, wherein the at least one pre-hospital setting is in a geographicregion, wherein the historical patient data were collected from at leastone trauma center in the geographic region, and wherein the ML model istailored to triage levels present in the geographic region based on thehistorical patient data collected from the at least one trauma center inthe geographic region.
 5. The method according to claim 4, wherein thehistorical patient data comprises the triage categories assigned to thepatients in the at least one pre-hospital setting and a dispositionreflecting accuracy of an initial triage level assigned in the at leastone pre-hospital setting.
 6. The method according to claim 4, whereinthe historical patient data were collected from the at least one traumacenter in the geographic region by elements within the at least onetrauma center.
 7. The method according to claim 4, wherein thehistorical patient data were collected from the at least one traumacenter in the geographic region by a third party.
 8. A triage system,comprising: a processor; a non-transitory computer-readable medium; andinstructions stored on the non-transitory computer-readable medium andtranslatable by the processor for: receiving patient data from devicesin a pre-hospital setting, the patient data describing a patient in thepre-hospital setting, the computer having a machine learning (ML) modelfor assigning a triage category to the patient based on a plurality ofinput variables associated with the patient, wherein the ML model istrained with the plurality of input variables based on historicalpatient data that describe patients who had been assigned triagecategories in at least one pre-hospital setting prior to the patientsbeing transported or delivered into care of one or more trauma centers,each of the one or more trauma centers having a level designationcorresponding to a respective one of the triage categories; applying theML model to the patient data received from the devices in thepre-hospital setting, wherein the applying comprises processing theplurality of input variables associated with the patient and generatinga triage category for the patient in the pre-hospital setting based onthe processing; and presenting, via a user interface, the triagecategory generated for the patient in the pre-hospital setting fortransporting or delivering the patient into care of a trauma centerhaving a level designation corresponding to the triage categorygenerated for the patient.
 9. The system of claim 8, wherein the MLmodel comprises a statistical model that includes a classifierconfigured for taking as input one or more attributes collected from thepatient and determining a triage category for the patient based on theone or more attributes collected from the patient.
 10. The system ofclaim 9, wherein the classifier is configured for processing a number ofinput variables fewer than the plurality of input variables and forgenerating a triage category for the patient in the pre-hospital settingbased on the number of input variables associated with the patient. 11.The system of claim 8, wherein the at least one pre-hospital setting isin a geographic region, wherein the historical patient data werecollected from at least one trauma center in the geographic region, andwherein the ML model is tailored to triage levels present in thegeographic region based on the historical patient data collected fromthe at least one trauma center in the geographic region.
 12. The systemof claim 11, wherein the historical patient data comprises the triagecategories assigned to the patients in the at least one pre-hospitalsetting and a disposition reflecting accuracy of an initial triage levelassigned in the at least one pre-hospital setting.
 13. The system ofclaim 11, wherein the historical patient data were collected from the atleast one trauma center in the geographic region by elements within theat least one trauma center.
 14. The system of claim 11, wherein thehistorical patient data were collected from the at least one traumacenter in the geographic region by a third party.
 15. A computer programproduct comprising a non-transitory computer-readable medium storinginstructions translatable by a processor for: receiving patient datafrom devices in a pre-hospital setting, the patient data describing apatient in the pre-hospital setting, the computer having a machinelearning (ML) model for assigning a triage category to the patient basedon a plurality of input variables associated with the patient, whereinthe ML model is trained with the plurality of input variables based onhistorical patient data that describe patients who had been assignedtriage categories in at least one pre-hospital setting prior to thepatients being transported or delivered into care of one or more traumacenters, each of the one or more trauma centers having a leveldesignation corresponding to a respective one of the triage categories;applying the ML model to the patient data received from the devices inthe pre-hospital setting, wherein the applying comprises processing theplurality of input variables associated with the patient and generatinga triage category for the patient in the pre-hospital setting based onthe processing; and presenting, via a user interface, the triagecategory generated for the patient in the pre-hospital setting fortransporting or delivering the patient into care of a trauma centerhaving a level designation corresponding to the triage categorygenerated for the patient.
 16. The computer program product of claim 15,wherein the ML model comprises a statistical model that includes aclassifier configured for taking as input one or more attributescollected from the patient and determining a triage category for thepatient based on the one or more attributes collected from the patient.17. The computer program product of claim 16, wherein the classifier isconfigured for processing a number of input variables fewer than theplurality of input variables and for generating a triage category forthe patient in the pre-hospital setting based on the number of inputvariables associated with the patient.
 18. The computer program productof claim 15, wherein the at least one pre-hospital setting is in ageographic region, wherein the historical patient data were collectedfrom at least one trauma center in the geographic region, and whereinthe ML model is tailored to triage levels present in the geographicregion based on the historical patient data collected from the at leastone trauma center in the geographic region.
 19. The computer programproduct of claim 18, wherein the historical patient data comprises thetriage categories assigned to the patients in the at least onepre-hospital setting and a disposition reflecting accuracy of an initialtriage level assigned in the at least one pre-hospital setting.
 20. Thecomputer program product of claim 18, wherein the historical patientdata were collected from the at least one trauma center in thegeographic region by elements within the at least one trauma center.